AI-based single-cell analysis as a next-generation histological grading approach in colorectal cancer: prognostic role and tumor biology assessment.

The management of colorectal carcinoma (CRC) relies on pathological interpretation. Digital pathology approaches allow for development of new potent AI-based prognostic parameters. The study aim was to develop an AI-based image analysis platform allowing fully automatized, quantitative, and explainable tumor microenvironment analysis and extraction of prognostic information from H&E-stained whole slide images (WSI) of CRC patients. Three well characterized, multi-institutional patient cohorts were included (patient n=1438, WSI n >2400). The developed image analysis platform implements quality control and established algorithms to segment tissue and detect cell types. It enabled systematic analysis of immune infiltrate, assessing its prognostic relevance, intratumoral heterogeneity, and biological concepts across multiple survival endpoints. Analyzing single cell types and their combinations reveals independent, prognostic parameters, highlighting significant intratumoral heterogeneity, especially in the biopsy setting, which must be accounted for. A key morphological concept related to tumor control by the immune system is described, resulting in a capable, independent prognostic parameter (tumor "out-of-control"). Our findings have direct clinical implications and can be used as a foundation for updating the existing CRC grading systems.